【導(dǎo)讀】project.approachedfroma"human-centereddesign"perspective.systems(FMS)andflightguidance(.,autopilots;

　　

【正文】 esearch plan to maximize human factors input for concurrent engineering of adaptive automation. Classification of Human Factors Issues. Kantowitz and Campbell (1996) identified some of the key human factors issues to be considered in the design of advanced automated systems. These include allocation of function, stimulusresponse patibility, and mental models. Scerbo (1996) further suggested the need for research on teams, munication, and training and practice in adaptive automated systems design. The impact of adaptive automation systems on monitoring behavior, situational awareness, skill degradation, and social dynamics also needs to be investigated. Generally however, Billings (1997) stated that the problems of automation share one or more of the following characteristics: Brittleness, opacity, literalism, clumsiness, monitoring requirement, and data overload. These characteristics should inform design guidelines for the development, analysis, and implementation of adaptive automation technologies. The characteristics are defined as: ? Brittleness refers to ...an attribute of a system that works well under normal or usual conditions but that does not have desired behavior at or close to some margin of its operating envelope. ? Opacity reflects the degree of understanding of how and why automation functions as it does. The term is closely associated with mode awareness (Sarter amp。 Woods, 1994), transparency。 or virtuality (Schneiderman, 1992). ? Literalism concern the narrowmindedness of the automated system。 that is, the flexibility of the system to respond to novel events. ? Clumsiness was coined by Wiener (1989) to refer to automation that reduced workload demands when the demands are already low (., transit flight phase), but increases them when attention and resources are needed elsewhere (., descent phase of flight). An 中英文資料 example is when the copilot needs to reprogram the FMS, to change the plane39。s descent path, at a time when the copilot should be scanning for other planes. ? Monitoring requirement refers to the behavioral and cognitive costs associated with increased supervisory control (Sheridan, 1987。 1991). ? Data overload points to the increase in information in modern automated contexts (Billings, 1997). These characteristics of automation have relevance for defining the scope of human factors issues likely to plague adaptive automation design if significant attention is not directed toward ensuring humancentered design. The human factors research munity has noted that these characteristics can lead to human factors issues of allocation of function (., when and how should functions be allocated adaptively)。 stimulusresponse patibility and new error modes。 how adaptive automation will affect mental models, situation models, and representational models。 concerns about mode unawareness and situation awareness decay。 manual skill decay and the “outoftheloop” performance problem。 clumsy automation and task/workload management。 and issues related to the design of automation. This last issue points to the significant concern in the human factors munity of how to design adaptive automation so that it reflects what has been called “teamcentered”。 that is, successful adaptive automation will likely embody the concept of the “electronic team member”. However, past research (., Pilots Associate Program) has shown that designing automation to reflect such a role has significantly different requirements than those arising in traditional automation design. The field is currently focused on answering the questions, “what is it that defines one as a team member?” and “how does that definition translate into designing automation to reflect that role?” Unfortunately, the literature also shows that the answer is not transparent and, therefore, adaptive automation must first tackle its own unique and difficult problems before it may be considered a viable prescription to current humanautomation interaction problems. The next section describes the concept of the electronic team member and then discusses the literature with regard to team dynamics, coordination, munication, shared mental models, and the implications of these for adaptive automation design. Adaptive Automation as Electronic Team Member Layton, Smith, and McCoy (1994) stated that the design of automated systems should be from a teamcentered approach。 the design should allow for the coordination between machine agents and human practitioners. However, many researchers have noted that automated systems tend to fail as team players (Billings, 1991。 Malin amp。 Schreckenghost, 中英文資料 1992。 Malin et al., 1991。 Sarter amp。 Woods, 1994。 Scerbo, 1994。 1996。 Woods, 1996). The reason is what Woods (1996) calls “apparent simplicity, real plexity.” Apparent Simplicity, Real Complexity. Woods (1996) stated that conventional wisdom about automation makes technology change seem simple. Automation can be seen as simply changing the human agent for a machine agent. Automation further provides for more options and methods, frees up operator time to do other things, provides new puter graphics and interfaces, and reduces human error. However, the reality is that technology change has often resulted in the design of automated systems that are strong, silent, clumsy, and difficult to direct. Woods (1996) stated that these types of systems are “are not team players. The literature has described these as: ? Strong automation refers to automation that act autonomously and possess authority. A number of researchers (Billings, 1997。 Norman, 1990。 Sarter amp。 Woods, 1994。 Wiener,1989) have noted that increased autonomy and authority creates new monitoring and coordination demands for operators of a system (Woods, 1996。 Scerbo, 1996). ? Automation can also